jl tests_website
This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.
# install.packages("testthat")
# install.packages("devtools")
# install.packages("dplyr")
# install.packages("ICC")
# install.packages("MetaUtility")
# install.packages("ggplot2")
library(testthat)
#library(EValue)
library(devtools)
library(dplyr)
library(ICC)
library(MetaUtility)
library(ggplot2)
library(boot)Below tests run on 11/10/20 with github code pull on 11/09/20 at 2:45pm:
source("~/Box Sync/jlee/Maya/evalue/EValue/tests/helper_testthat.R")
source("~/Box Sync/jlee/Maya/evalue/EValue/R/meta-analysis.R")
setwd("~/Box Sync/jlee/Maya/evalue/tests_human_inspection/")test1 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
confounded_meta(q = log(.9),
r = 0.1,
muB = 0,
tail = "below",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.## Warning in confounded_meta(q = log(0.9), r = 0.1, muB = 0, tail = "below", :
## Phat is already less than or equal to r even with no confounding, so Tmin
## and Gmin are simply equal to 1. No confounding at all is required to make the
## specified shift.### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Show Traceback
#
# Rerun with Debug
# Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G) :
# arguments imply differing number of rows: 1, 0
### Website output:
knitr::include_graphics("jl_website_test1a.png")
sens_plot(type = "line",
q = log(0.9),
tail = "below",
Bmin = log(1),
Bmax = log(4),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 4.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning in sens_plot(type = "line", q = log(0.9), tail = "below", Bmin =
## log(1), : Some of the pointwise confidence intervals were not estimable via
## bias-corrected and accelerated bootstrapping, so the confidence band on the plot
## may not be shown for some values of the bias factor. You can try increasing R.
### R output:
# Warning message:
# In sens_plot(type = "line", q = log(0.9), tail = "below", Bmin = log(1), :
# Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. You can try increasing R.
### Website output:
knitr::include_graphics("jl_website_test1b.png")
test2 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
confounded_meta(q = log(.5),
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 0.001951293 0.9858491 1.000000
# 2 Tmin 2.133700 0.025066232 2.0871944 2.183581
# 3 Gmin 3.689005 0.051400052 3.5935762 3.791202
# Warning message:
# In norm.inter(t, adj.alpha) : extreme order statistics used as endpoints
### Website output:
knitr::include_graphics("jl_website_test2a.png")
sens_plot(type = "line",
q = log(.5),
tail = "above",
Bmin = log(1),
Bmax = log(6),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 2.
## Warning: extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 3.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 4.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 5.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 8.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning in sens_plot(type = "line", q = log(0.5), tail = "above", Bmin =
## log(1), : Some of the pointwise confidence intervals were not estimable via
## bias-corrected and accelerated bootstrapping, so the confidence band on the plot
## may not be shown for some values of the bias factor. You can try increasing R.
### R output:
# Warning message:
# In sens_plot(type = "line", q = log(0.5), tail = "above", Bmin = log(1), :
# None of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing R.
### Website output:
knitr::include_graphics("jl_website_test2b.png")
test3 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
## get error for column name
confounded_meta(q = log(.5),
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vyi",
dat = d,
R = 2000)## Error in Phat_causal(q = q, B = muB, tail = tail, dat = dat, yi.name = yi.name, : dat does not contain a column named vi.name### R output:
# Error in Phat_causal(q = q, B = muB, tail = tail, dat = dat, yi.name = yi.name, :
# dat does not contain a column named vi.name
### Website output:
knitr::include_graphics("jl_website_test3.png")
test4 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
## on log-RR scale:
# log(.5)
confounded_meta(q = -0.6931472,
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.8285714 0.03891938 0.7385156 0.8928571
# 2 Tmin 1.8341568 0.02953164 1.7897376 1.9005732
# 3 Gmin 3.0710782 0.06134111 2.9786123 3.2088566
### Website output:
knitr::include_graphics("jl_website_test4a.png")
sens_plot(type = "line",
q = -0.6931472,
tail = "above",
Bmin = 1,
Bmax = 4,
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## Warning in sens_plot(type = "line", q = -0.6931472, tail = "above", Bmin = 1, :
## None of the pointwise confidence intervals were not estimable via bias-corrected
## and accelerated bootstrapping, so the confidence band on the plot is omitted.
## You can try increasing R.
### R output:
# Warning message:
# In sens_plot(type = "line", q = -0.6931472, tail = "above", Bmin = 1, :
# None of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing R.
### Website output:
knitr::include_graphics("jl_website_test4b.png")
test5 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
## extreme R?
confounded_meta(q = log(.5),
r = 0.1,
muB = .5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 10000)### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.8285714 0.03971533 0.7357143 0.8928571
# 2 Tmin 2.1777251 0.08610269 2.0699317 2.3537022
# 3 Gmin 3.7792124 0.17615242 3.5581137 4.1386985
### Website output:
knitr::include_graphics("jl_website_test5.png")
test6 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
confounded_meta(q = log(1.2),
r = 1.0,
muB = 0,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G): arguments imply differing number of rows: 1, 0### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Show Traceback
#
# Rerun with Debug
# Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G) :
# arguments imply differing number of rows: 1, 0
### Website output:
knitr::include_graphics("jl_website_test6.png")
test7 data_calib_test_1-1.csv file
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## all 0
confounded_meta(q = log(0),
r = 0,
muB = 0,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 0)## [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.## [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"## The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 1 NA NA NA
# 2 Tmin Inf NA NA NA
# 3 Gmin NaN NA NA NA
### Website output:
knitr::include_graphics("jl_website_test7a.png")
sens_plot(type = "line",
q = log(0),
tail = "above",
Bmin = log(0),
Bmax = log(0),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 0)## Error in seq.default(Bmin, Bmax, 0.01): 'from' must be a finite number### R output:
# Error in seq.default(Bmin, Bmax, 0.01) : 'from' must be a finite number
### Website output:
knitr::include_graphics("jl_website_test7b.png")
test8 data_calib_test_1-1.csv file
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## parametric method test
## vt2 = prop*t2^2
confounded_meta(method="parametric",
q=log(1.1),
r=0.2,
tail="above",
muB=log(1.2),
sigB=0,
yr=log(1.2),
vyr=0.01^2,
t2=0.1,
vt2=0.35*(0.1^2))### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.3815558 0.03606286 0.3108739 0.4522377
# 2 Tmin 1.4235523 0.11297173 1.2021318 1.6449728
# 3 Gmin 2.2000501 0.24733823 1.7152761 2.6848242
### Website output:
knitr::include_graphics("jl_website_test8a.png")
sens_plot(method = "parametric",
type="line",
q=log(1.1),
yr=log(1.2),
vyr=0.01^2,
t2=0.1,
vt2=0.35*(0.1^2),
Bmin=log(1),
Bmax=log(4),
sigB=0,
tail="above" )## Warning in sens_plot(method = "parametric", type = "line", q = log(1.1), :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(1.1), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test8b.png")
test9 data_calib_test_1-1.csv file
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## parametric method test
## vt2 = prop*t2^2
## see what errors if all 0
confounded_meta(method="parametric",
q=log(0),
r=0,
tail="above",
muB=log(0),
sigB=0,
yr=log(0),
vyr=0^2,
t2=0,
vt2=0*(0^2))## Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", : Must have t2 > sigB^2### R output:
# Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", :
# Must have t2 > sigB^2
### Website output:
knitr::include_graphics("jl_website_test9.png")
test10 kodama_prepped.csv
d = read.csv("Datasets for website test/kodama_prepped.csv")
## parametric method test
## vt2 = prop*t2^2
confounded_meta(method="parametric",
q=log(.5),
r=0.75,
tail="below",
muB=log(1.5),
sigB=.25,
yr=log(1.5),
vyr=0.1^2,
t2=0.25,
vt2=0.5*(0.25^2))## Warning in confounded_meta(method = "parametric", q = log(0.5), r = 0.75, : Phat
## is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively
## using bias-corrected and accelerated bootstrapping to estimate all inference in
## this case.## Warning in sqrt(Tmin^2 - Tmin): NaNs produced### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.05471561 0.0874276 0 0.2260706
# 2 Tmin 0.23791135 0.3332082 1 0.8909875
# 3 Gmin NaN NaN NaN NaN
# Warning messages:
# 1: In confounded_meta(method = "parametric", q = log(0.5), r = 0.75, :
# Phat is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
# 2: In sqrt(Tmin^2 - Tmin) : NaNs produced
### Website output:
knitr::include_graphics("jl_website_test10a.png")
sens_plot(method = "parametric",
type="line",
q=log(.5),
yr=log(1.5),
vyr=0.1^2,
t2=0.25,
vt2=0.5*(0.25^2),
Bmin=log(1),
Bmax=log(4),
sigB=0,
tail="below" )## Warning in sens_plot(method = "parametric", type = "line", q = log(0.5), :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(0.5), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test10b.png")
test11 kodama_prepped.csv
d = read.csv("Datasets for website test/kodama_prepped.csv")
## on log-RR scale
# log(1.2)
## parametric method test
## vt2 = prop*t2^2
confounded_meta(method="parametric",
q=0.1823216,
r=0.2,
tail="below",
muB=.2,
sigB=.25,
yr=.4,
vyr=0.1^2,
t2=0.25,
vt2=0.1*(0.25^2))### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.4837171 0.09211893 0.3031673 0.6642669
# 2 Tmin 1.2252345 0.22744734 1.0000000 1.6710231
# 3 Gmin 1.7505582 0.54144924 1.0000000 2.8117792
### Website output:
knitr::include_graphics("jl_website_test11a.png")
sens_plot(method = "parametric",
type="line",
q=0.1823216,
yr=.4,
vyr=0.1^2,
t2=0.25,
vt2=0.1*(0.25^2),
Bmin=1,
Bmax=6,
sigB=.25,
tail="below" )## Warning in sens_plot(method = "parametric", type = "line", q = 0.1823216, :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = 0.1823216, :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test11b.png")
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.